CN109085291A - It lacks component iterative inversion and demarcates nesting-PMF source resolution algorithm - Google Patents
It lacks component iterative inversion and demarcates nesting-PMF source resolution algorithm Download PDFInfo
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- 229910001385 heavy metal Inorganic materials 0.000 claims description 4
- 229910052700 potassium Inorganic materials 0.000 claims description 4
- 229910052718 tin Inorganic materials 0.000 claims description 4
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Abstract
The present invention provides missing component iterative inversions to demarcate nesting-PMF source resolution algorithm.Include: to construct multicomponent online data using online monitoring instruments, is input to positive definite factor matrix decomposition model (PMF)；Model calculating parameter is arranged in selective factor B number；Model calculating is carried out, extraction factor calculates each factor contributions；In conjunction with actual measurement derived components spectrum, factor component spectrum and factor contributions, inverse receptor Si and Al, the reconstruct data receptor for obtaining Si and Al respectively and reconstruct are by volume matrix X1；It will reconstruct by volume matrix X1It is re-entered into model and is calculated, obtain new factor spectrum and factor contributions, compose inverse receptor Si and Al in conjunction with actual measurement derived components, the reconstruct data receptor for obtaining Si and Al and reconstruct are by volume matrix X2；It repeats the above steps until obtaining the reconstruct data receptor for meeting restrictive condition.Missing component iterative inversion calibration nesting-PMF source resolution algorithm provided by the invention can restore actual receptor data to a certain degree, improve the accuracy that model calculates.
Description
Technical field
The present invention relates to Source Apportionment of Atmospheric Particulate fields, and in particular to a kind of missing component iterative inversion calibration nesting-
PMF source resolution algorithm.
Background technique
In The Atmosphere Over China pollution condition is more serious, and the Source Apportionment using science judges PM2.5Source is control and administers
Key.To administer serious atmospheric pollution, the 1970s, the U.S. took the lead in carrying out the research of particulate matter origin analysis, 20th century
The nineties, the correlative study in Europe also had remarkable break-throughs.Source resolution work in China's is started in the 1980s.
PM at present2.5Source resolution is based on offline filter membrane sampling, and the sampling time is generally 24 hours or longer, from sample
The acquisition period that acquisition, chemical group analyze model result is long, and the data equalized in certain time can not capture small time ruler
Spend the high density pollution process of (such as a few minutes or a few houres), it is difficult to meet to the heavily contaminated Event origin parsing that happens suddenly in the short time
Demand.The a variety of particulate matter on-line instruments developed in recent years can provide the real-time concentration of certain chemical constituents and tracer,
Online source resolution can provide important decision service to formulate quickly and efficiently control measure, be non-origin analysis job development
Important directions.
Compared with off-line monitoring technology, chemical constituent type is less in the particulate matter of on-line monitoring technique monitoring, such as lacks
Lose the important logos components such as Si, Al.The missing of this kind of mark component may result in being total to for the sources class such as soil, fire coal, motor vehicle
It is linearly increasing, to will increase the uncertainty of the factor component spectrum and source contribution in this few class source.
Summary of the invention
Present invention aim to address chemical constituents in the particulate matter of existing on-line monitoring technique monitoring to lack important mark
Know component, such as the earth's crust identifies component Si, Al, the missing of this kind of mark component leads to being total to for the sources class such as soil, fire coal, motor vehicle
Linearly increasing problem.The various numbers needed in receptor source resolution model are measured based on the measuring instrument compared with high time resolution
According to binding factor analysis model and practical local derived components spectrum provide one kind based on PMF Factor Analysis Model, iteration is anti-
Drill the method that calibration nesting inverse Si and Al obtain the reconstruct data receptor containing Si and Al.Method provided by the invention can optimize
Source resolution is as a result, improve the accuracy that model calculates.
Missing component iterative inversion provided by the invention demarcates nesting-PMF Source Apportionment, the specific steps are as follows:
The model that step 1, the particle concentration based on different instrument monitorings and its chemical constituent online monitoring data are constituted
Input data, including particle concentration, water soluble ion, carbon component and concentration of element data；
Particle concentration refers to the PM measured by particulate matter online monitoring instruments2.5Concentration；Water soluble ion is by online ion
Chromatograph measurement, including NH4 +、Na+、K+、Ca2+、Mg+、SO4 2-、NO3 -And Cl-；Carbon component is surveyed by semicontinuous OC/EC instrument
Amount, including OC and EC；Element is monitored by heavy metal online analyzer, including K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga,
As, Se, Ag, Cd, Sn, Sb, Ba, Au, Hg, Tl, Pb and Bi component, but lack the important earth's crust mark component Si and Al.
Step 2, setting model parameter；
The Factor Analysis Model is positive definite factor matrix decomposition model (PMF), and the parameter for needing to set includes component
Uncertainty and factor number, shown in the uncertain setting method such as formula (1) of component or (2)；
If concentration of component≤minimum detection limit MDL, indeterminacy of calculation are as follows:
Unc=5/6*MDL (1)
If concentration of component > minimum detection limit MDL, indeterminacy of calculation are as follows:
In formula, Unc indicates the uncertainty of component；Error Fraction is difference scores, according to specific sampling and
Situation is analyzed to set；Concentration is concentration of component；
What factor number indicated is the number of pollution sources, observes the actual conditions setting of point as needed, and the factor is set
Fixed number is less than the quantity of chemical constituent in input data.
Step 3, carry out model calculating, according to input data receptor and relevant parameter be arranged, be calculated factor spectrum F with
Factor contributions matrix G；
Step 4 obtains Si in conjunction with actual measurement derived components spectrum, factor component spectrum and factor contributions, inverse receptor Si and Al respectively
Data receptor j is reconstructed with the first time of Al1i、k1iAnd reconstruct for the first time is by volume matrix X1；Shown in working principle such as formula (3):
F0* G=X1 (3)
In formula, F0It is the combination of factor spectrum and actual measurement derived components spectrum, factor spectrum is that step 3 obtains, and does not contain Si and Al；
G is the factor contributions matrix that step 3 obtains, X1It is the data receptor of reconstruct for the first time, wherein containing the Si of reconstruct for the first time
With Al data receptor j1i、k1i。
Step 5 reconstructs got in step 4 by volume matrix X for the first time1It is re-entered into positive definite factor matrix and decomposes mould
Type (PMF) is calculated, and new factor spectrum and factor contributions is obtained, and recombines actual measurement derived components spectrum inverse receptor Si and Al,
Second of reconstruct data receptor j of Si and Al is obtained respectively2i、k2iAnd second of reconstruct is by volume matrix X2；
Step 6 repeats step 1 to step 5, until obtaining n-th reconstruct data receptor jni、kniAnd reconstruct receptor square
Battle array XnMeet restrictive condition:
In formula, i is represented i-th of receptor (i.e. the i-th row), and m is receptor number (receptor matrix line number), and n is calculation times, p,
Q is respectively restrictive condition, and numerical values recited rule of thumb judges, related with real receptor environment and data receptor total amount.
The advantages of the present invention:
The present invention can demarcate nesting method by iterative inversion and obtain containing dense closest to Si and Al in real receptor environment
The reconstruct data receptor of degree makes up the deficiency that existing online data lacks the important earth's crust mark component Si and Al.To reconstruct receptor
Data carry out source resolution, can optimize source resolution as a result, improving the accuracy that model calculates.
Detailed description of the invention
Fig. 1 is model calculation process block diagram of the present invention.
Specific embodiment
Embodiment 1:
Referring to attached drawing 1, this example carries out model calculating using online monitoring data and Factor Analysis Model, and specific steps are such as
Under:
Step 1 constructs Factor Analysis Model input data.Particulate matter of the input data based on different instrument monitorings
Concentration and its chemical constituent online monitoring data are constituted, including water soluble ion, carbon component, concentration of element, particle concentration.
PM is measured using particulate matter online monitoring instruments2.5Concentration.
Using semicontinuous OC/EC apparatus measures carbon component, the concentration including OC and EC.
Water soluble ion, including NH are measured using online ion-chromatographic analyzer4 +、Na+、Mg2+、K+、Ca2+、 SO4 2-、
NO3 -、Cl-Deng concentration.
Using heavy metal online analyzer monitoring elements, including K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As,
(group of each input data is sub-category according to actual monitoring for the concentration of Se, Ag, Cd, Sn, Sb, Ba, Au, Hg, Tl, Pb and Bi etc.
Data have certain variation).
From 22 days 0 July in 2014 up to 31 days 23 July in 2014 when continuous sampling, the data time resolution ratio of monitoring is 1
Hour, data receptor 240 are obtained altogether, include NH4 +、Na+、Mg2+、SO4 2-、 NO3 -、K-、Ca、Cr、Mn、Fe、Ni、Cu、Zn、
Pb, OC, EC totally 16 kinds of components.
Mode input parameter is arranged in step 2, and the Factor Analysis Model that the present invention uses is positive definite factor matrix decomposition model
(PMF), the parameter for needing to input Factor Analysis Model includes the row, column number of input data, the factor number and model meter of extraction
The uncertain parameters of calculation.The present embodiment input is provided that
Line number: 240 rows；Columns: 16 column；The factor number of extraction: 5；The uncertain parameters that model calculates: 0.25.
Step 3 carries out model calculating, is arranged according to the data receptor of step 1 input and second step relevant parameter, is calculated
Obtain factor component spectrum matrix F and factor contributions matrix G；The factor spectrum matrix F of extraction is as shown in table 1, factor contributions matrix G
As shown in table 2 (since data volume is excessive, only intercept 22 days 0 July in 2014 when 24 days 23 July in 2014 amount to 72 by
For volume data).In conjunction with Tables 1 and 2, the source class that rule of thumb the artificial judgement factor represents.The factor 1 is fugitive dust source, the factor 2
For two nitroxylates, the factor 3 is coal-fired source, and the factor 4 is secondary source of sulfuric acid, and the factor 5 is motor vehicle source.
1 source resolution factor component of table composes F (ug/m3)
2 source resolution factor contributions concentration G (μ g/m of table3)
Step 4 combines actual measurement derived components spectrum, factor component spectrum and factor contributions, inverse receptor Si and Al to obtain Si respectively
Data receptor j is reconstructed with the first time of Al1i、k1iAnd reconstruct for the first time is by volume matrix X1；Wherein, actual measurement derived components spectrum uses certain
Area actual measurement derived components spectrum, table 3 are somewhere fire coal source, fugitive dust, motor vehicle derived components spectrum；Due to the Si and Al of other source emissions
Content is lower, i.e., Si and Al concentration is very low in derived components spectrum, does not consider here.Source contribution matrix (the table that will be obtained in step 3
2) the coal-fired source, fugitive dust source, motor vehicle source contribution concentration-time sequence in and Al, Si value phase in corresponding source component spectrum (table 3)
Multiply, obtains the receptor Si and Al concentration-time sequence j of reconstruct1i、k1i(table 4, still with 22 days 0 July in 2014 up to 2014 7
When the moon 24 days 23 for total 72 data receptors)；It is merged with original input data, i.e. reconstruct of the acquisition containing Si and Al
Data receptor X1。
3 Tianjin fire coal source of table, fugitive dust source, motor vehicle component spectrum (g/g) in a steady stream
The receptor Si Al concentration j that table 4 reconstructs1i、k1i(μg/m3)
Step 5 is by got in step 4 reconstruct for the first time by volume matrix X1It is re-entered into positive definite factor matrix and decomposes mould
Type (PMF) is calculated, and new factor spectrum and factor contributions is obtained, and recombines actual measurement derived components spectrum inverse receptor Si and Al,
Second of reconstruct data receptor j of Si and Al is obtained respectively2i、k2iAnd second of reconstruct is by volume matrix X2；
Step 6 repeats step 1 to step 5, until obtaining n-th reconstruct data receptor jni、kniAnd reconstruct receptor square
Battle array XnMeet restrictive condition:
In formula, i is represented i-th of receptor (i.e. the i-th row), and m is receptor number (receptor matrix line number), and n is calculation times, p,
Q is respectively restrictive condition.In the implementation case, Si, Al concentration and iterate to calculate for the 9th time that the 8th iterative calculation obtains
To Si, Al concentration (still list as shown in table 5 and amount to 72 receptors when 24 days 23 July in 2014 22 days 0 July in 2014
Data instance).Si, Al concentration j that 8th time and the 9th time reconstruct obtains8i、k8iAnd j9i、k9iMeet restrictive condition respectively:
In formula, m=240 (240 data receptors), n=9 (the 9th iteration), p=20, q=10 are (rule of thumb and receptor
Sum setting)；So far the 9th iteration result for obtaining meeting restrictive condition is reconstructed by volume matrix X9。
The the 8th, the 9 receptor Si Al concentration j that table 5 reconstructs8i、k8iAnd j9i、k9i(μg/m3)
Embodiment 2:
Referring to attached drawing 1, this example carries out model calculating using online monitoring data and Factor Analysis Model, and specific steps are such as
Under:
Step 1 constructs Factor Analysis Model input data.The input data includes water soluble ion, carbon component, member
Plain concentration, particle concentration.
PM is measured using particulate matter online monitoring instruments2.5Concentration.
Using semicontinuous OC/EC apparatus measures carbon component, the concentration including OC and EC.
Water soluble ion, including NH are measured using online ion-chromatographic analyzer4 +、Na+、Mg2+、K+、Ca2+、 SO4 2-、
NO3 -、Cl-Deng concentration.
Using heavy metal online analyzer monitoring elements, including K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As,
(group of each input data is sub-category according to actual monitoring for the concentration of Se, Ag, Cd, Sn, Sb, Ba, Au, Hg, Tl, Pb and Bi etc.
Data have certain variation).
From on August 1,0 2014 up on August 3,23 2014 when continuous sampling, the data time resolution ratio of monitoring is 1 small
When, data receptor 72 are obtained altogether, include NH4 +、Na+、Mg2+、SO4 2-、NO3 -、 K-、Ca、Cr、Mn、Fe、Ni、Cu、Zn、Pb、
OC, EC totally 16 kinds of components.
Mode input parameter is arranged in step 2, and the Factor Analysis Model that the present invention uses is positive definite factor matrix decomposition model
(PMF), the parameter for needing to input Factor Analysis Model includes the row, column number of input data, the factor number and model meter of extraction
The uncertain parameters of calculation.The present embodiment input is provided that
Line number: 72 rows；Columns: 16 column；The factor number of extraction: 5；The uncertain parameters that model calculates: 0.23.
Step 3 carries out model calculating, is arranged according to the data receptor of step 1 input and second step relevant parameter, is calculated
Obtain factor component spectrum matrix F and factor contributions matrix G；The factor spectrum matrix F of extraction is as shown in table 6, factor contributions matrix G
As shown in table 7.In conjunction with table 6 and table 7, the source class that rule of thumb the artificial judgement factor represents.The factor 1 is motor vehicle source, the factor 2
For two nitroxylates, the factor 3 is coal-fired source, and the factor 4 is fugitive dust source, and the factor 5 is two sulfoxylates.
6 source resolution factor component of table composes F (ug/m3)
7 source resolution factor contributions concentration G (μ g/m of table3)
Step 4 combines actual measurement derived components spectrum, factor component spectrum and factor contributions, inverse receptor Si and Al to obtain Si respectively
Data receptor j is reconstructed with the first time of Al1i、k1iAnd reconstruct for the first time is by volume matrix X1；Wherein, actual measurement derived components spectrum uses certain
Area actual measurement derived components spectrum, table 8 are somewhere fire coal source, fugitive dust, motor vehicle derived components spectrum；Due to the Si and Al of other source emissions
Content is lower, i.e., Si and Al concentration is very low in derived components spectrum, does not consider here.Source contribution matrix (the table that will be obtained in step 3
7) the coal-fired source, fugitive dust source, motor vehicle source contribution concentration-time sequence in and Al, Si value phase in corresponding source component spectrum (table 8)
Multiply, obtains the receptor Si and Al concentration-time sequence j of reconstruct1i、k1i(table 9)；It is merged with original input data, that is, is obtained
Reconstruct data receptor X containing Si and Al1。
8 Tianjin fire coal source of table, fugitive dust source, motor vehicle component spectrum (g/g) in a steady stream
The receptor Si Al concentration j that table 9 reconstructs1i、k1i(μg/m3)
Step 5 is by got in step 4 reconstruct for the first time by volume matrix X1It is re-entered into positive definite factor matrix and decomposes mould
Type (PMF) is calculated, and new factor spectrum and factor contributions is obtained, and recombines actual measurement derived components spectrum inverse receptor Si and Al,
Second of reconstruct data receptor j of Si and Al is obtained respectively2i、k2iAnd second of reconstruct is by volume matrix X2；
Step 6 repeats step 1 to step 5, until obtaining n-th reconstruct data receptor jni、kniAnd reconstruct receptor square
Battle array XnMeet restrictive condition:
In formula, i is represented i-th of receptor (i.e. the i-th row), and m is receptor number (receptor matrix line number), and n is calculation times, p,
Q is respectively restrictive condition.In the implementation case, Si, Al concentration and iterate to calculate for the 6th time that the 5th iterates to calculate
Si, Al concentration arrived is as shown in table 10.Si, Al concentration j that the 5th and the 6th reconstruct obtain5i、k5iAnd j6i、k6iMeet respectively
Restrictive condition:
In formula, m=72 (72 data receptors), n=6 (the 6th iteration), (rule of thumb and receptor is total by p=5, q=5
Setting)；So far the 6th iteration result for obtaining meeting restrictive condition is reconstructed by volume matrix X6。
The the 5th, the 6 receptor Si Al concentration j that table 10 reconstructs5i、k5iAnd j6i、k6i(μg/m3)
Claims (5)
1. a kind of missing component iterative inversion demarcates nesting-PMF source resolution algorithm, it is characterised in that the described method includes:
Step 1, the particulate matter and its concentration of component monitored using online monitoring instruments, are constructed multicomponent receptor matrix X, are input to
Positive definite factor matrix decomposition model PMF；
Model calculating parameter is arranged in step 2, selective factor B number；
Step 3 carries out model calculating, and extraction factor calculates factor contributions；
Step 4 obtains Si and Al in conjunction with actual measurement derived components spectrum, factor component spectrum and factor contributions, inverse receptor Si and Al respectively
First time reconstruct data receptor j1i、k1iAnd reconstruct for the first time is by volume matrix X1；
Step 5, by the reconstruct in step 4 by volume matrix X1Positive definite factor matrix decomposition model PMF is re-entered into be calculated,
New factor spectrum and factor contributions is obtained, actual measurement derived components spectrum inverse receptor Si and Al is recombined, obtains Si's and Al respectively
Second of reconstruct data receptor j2i、k2iAnd second of reconstruct is by volume matrix X2。
Step 6 repeats above-mentioned step 1 to step 5, until obtaining n-th reconstruct data receptor jni、kniAnd reconstruct is by volume matrix
XnMeet restrictive condition:
In formula, i represents XiI-th of receptor in matrix, m are receptor number, and n is iterative calculation number, and p, q are respectively restrictive condition
Parameter.
2. missing component iterative inversion demarcates nesting-PMF source resolution algorithm as described in claim 1, it is characterised in that wherein
The input of online data is the mould that particle concentration and its chemical constituent online monitoring data based on different instrument monitorings are constituted
Type input data, including particle concentration, water soluble ion, carbon component and concentration of element data；
Particle concentration refers to the PM measured by particulate matter online monitoring instruments2.5Concentration；Water soluble ion is by online ion chromatography
Analyzer measurement, including NH4 +、Na+、K+、Ca2+、Mg+、SO4 2-、NO3 -And Cl-；Carbon component by semicontinuous OC/EC apparatus measures,
Including OC and EC；Element is monitored by heavy metal online analyzer, including K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As,
Se, Ag, Cd, Sn, Sb, Ba, Au, Hg, Tl, Pb and Bi component, but lack the important earth's crust mark component Si and Al.
3. missing component iterative inversion demarcates nesting-PMF source resolution algorithm as described in claim 1, it is characterised in that institute
The positive definite factor matrix decomposition model PMF stated, the parameter for needing to set include the uncertainty and factor number of component, component
Shown in uncertain setting method such as formula (1) or (2)；
If concentration of component≤minimum detection limit MDL, indeterminacy of calculation are as follows:
Unc=5/6*MDL (1)
If concentration of component > minimum detection limit MDL, indeterminacy of calculation are as follows:
In formula, Unc indicates the uncertainty of component；Error Fraction is difference scores, according to specific sampling and analysis
Situation is set；Concentration is concentration of component；
What factor number indicated is the number of pollution sources, and the actual conditions for observing point as needed are set, and factor setting
Number is less than the quantity of chemical constituent in input data.
4. missing component iterative inversion demarcates nesting-PMF source resolution algorithm as described in claim 1, it is characterised in that use
Positive definite factor matrix decomposition model PMF, according to input data receptor and relevant parameter be arranged, be calculated factor spectrum F and because
Son contribution matrix G.
5. missing component iterative inversion demarcates nesting-PMF source resolution algorithm as described in claim 1, it is characterised in that the 4th
In step, inverse receptor Si and Al obtain the reconstruct data receptor containing Si and Al, shown in working principle such as formula (3):
F0* G=X1 (3)
In formula, F0It is the combination of factor spectrum and actual measurement derived components spectrum, factor spectrum is that step 3 obtains, and does not contain Si and Al；G is
The factor contributions matrix that 3 steps obtain, X1Be for the first time reconstruct data receptor, wherein contain for the first time reconstruct Si and Al by
Volume data j1i、k1i。
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112614553A (en) * | 2020-12-29 | 2021-04-06 | 南开大学 | VOCs source analysis method based on chemical kinetics-receptor model fusion technology |
| CN114544894A (en) * | 2022-01-26 | 2022-05-27 | 中国科学院新疆生态与地理研究所 | PMF model-based source analysis uncertainty analysis method and system |
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